ReviewPharmaceuticals (Basel, Switzerland)2025
Regulatory Perspectives for AI/ML Implementation in Pharmaceutical GMP Environments.
Review in Pharmaceuticals (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical manufacturing of adipose-derived stem cells for therapeutic use: current technologies, quality systems and production challenges.Regenerative medicinePooled it
- Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks.The AAPS journal · 2026Review
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Article
- A Machine Learning Platform for Imputing Missing Critical Quality Attributes in Pharmaceutical Development.The AAPS journal · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- A Risk-Tiered Validation Framework for Artificial Intelligence in Drug Discovery: From Reproducibility to Clinical Translation.International journal of molecular sciences · 2026Review
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and Systematic Review (2015-2025).Pharmaceutics · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Artificial Intelligence in Radiology: Hidden Fragilities and the Path to Resilience.Saudi medical journal · 2026Review
- Genetic Insights into the Economic Toll of Cell Line Misidentification: A Comprehensive Review.Medical sciences (Basel, Switzerland) · 2026Review
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
- Artificial intelligence in vaccine development: applications, implementation, and future directions.Frontiers in cellular and infection microbiology · 2026Review
- Recent Developments in Pharmaceutical Spray Drying: Modeling, Process Optimization, and Emerging Trends with Machine Learning.Pharmaceutics · 2025Review
- Advanced microfluidic techniques for the preparation of solid lipid nanoparticles: Innovations and biomedical applications.International journal of pharmaceutics: X · 2025Review
- Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes.Pharmaceuticals (Basel, Switzerland) · 2025Review
- mRNA and DNA-Based Vaccines in Genitourinary Cancers: A New Frontier in Personalized Immunotherapy.Vaccines · 2025Review
- Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable.Therapeutic advances in drug safety · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrating artificial intelligence (AI) and machine learning (ML) into pharmaceutical manufacturing processes holds great promise for enhancing efficiency, product quality, and regulatory compliance. However, implementing good manufacturing practices (GMP) in regulated environments introduces complex challenges related to validation, data integrity, risk management, and regulatory oversight. This review article comprehensively analyzes current regulatory frameworks and guidance for AI/ML in pharmaceutical Good Manufacturing Practice (GMP) settings, identifies gaps and uncertainties, and proposes considerations for future policy development. Emphasis is placed on understanding regulatory expectations across various agencies, including the US FDA, EMA, and MHRA. This article examines verified case studies and pilot programs that demonstrate the successful application of AI/ML under regulatory scrutiny, as well as recent developments in regulatory frameworks and implementation strategies. Ultimately, this article emphasizes the importance of a risk-based life cycle approach and the need for advancements in regulatory science to accommodate the dynamic nature of AI/ML technologies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.